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Type : Classification
User : lmn
Analysis name: my-cclassificatio-demo_Auto select best model_20200529
Model selection : Auto select best model
Date time : 2020-05-29 14:35:36.111660
Input data: credit
LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_1 PAY_2 PAY_3 PAY_4 PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 default
0 20000 2 2 1 24 2 2 -1 -1 -2 ... 0.0 0.0 0.0 0.0 689.0 0.0 0.0 0.0 0.0 1
1 90000 2 2 2 34 0 0 0 0 0 ... 14331.0 14948.0 15549.0 1518.0 1500.0 1000.0 1000.0 1000.0 5000.0 0
2 50000 2 2 1 37 0 0 0 0 0 ... 28314.0 28959.0 29547.0 2000.0 2019.0 1200.0 1100.0 1069.0 1000.0 0
3 50000 1 2 1 57 -1 0 -1 0 0 ... 20940.0 19146.0 19131.0 2000.0 36681.0 10000.0 9000.0 689.0 679.0 0
4 50000 1 1 2 37 0 0 0 0 0 ... 19394.0 19619.0 20024.0 2500.0 1815.0 657.0 1000.0 1000.0 800.0 0

5 rows × 24 columns

Out[3]:
(24000, 24)
Data for Modeling: (22800, 24)
Unseen Data For Predictions: (1200, 24)

Preproccessing and EDA results


Models performance

Model Accuracy AUC Recall Prec. F1 Kappa
0 Ridge Classifier 0.82 0 0.3524 0.6806 0.4641 0.3688
1 Linear Discriminant Analysis 0.8198 0.7625 0.3686 0.6685 0.4749 0.3769
2 Light Gradient Boosting Machine 0.8193 0.7717 0.3703 0.666 0.4754 0.3769
3 Extreme Gradient Boosting 0.819 0.7812 0.3524 0.6755 0.4626 0.3665
4 Gradient Boosting Classifier 0.8187 0.7808 0.3578 0.6696 0.4659 0.3686
5 CatBoost Classifier 0.8182 0.7738 0.3691 0.6614 0.4732 0.3738
6 Ada Boost Classifier 0.8173 0.77 0.3329 0.6774 0.446 0.3517
7 Extra Trees Classifier 0.8047 0.7375 0.364 0.5959 0.4515 0.3412
8 Random Forest Classifier 0.8016 0.7212 0.3119 0.5994 0.4097 0.3047
9 Logistic Regression 0.7784 0.6496 0.0008 0.25 0.0017 0.0001
10 K Neighbors Classifier 0.7514 0.6027 0.1762 0.3706 0.2386 0.112
11 Decision Tree Classifier 0.7211 0.6102 0.4108 0.3801 0.3946 0.2139
12 SVM - Linear Kernel 0.6816 0 0.228 0.2121 0.1727 0.0416
13 Quadratic Discriminant Analysis 0.6267 0.7319 0.5782 0.4422 0.397 0.2093
14 Naive Bayes 0.3496 0.6377 0.9119 0.2423 0.3828 0.0512
Best suggested model based on best value for Ridge Classifier is Accuracy
Selected Ridge Classifier based on Accuracy

Selected models training results & plots

Accuracy AUC Recall Prec. F1 Kappa
0 0.8158 0.0 0.3456 0.6595 0.4535 0.3555
1 0.8252 0.0 0.3654 0.7011 0.4804 0.3876
2 0.8283 0.0 0.3768 0.7112 0.4926 0.4008
3 0.8308 0.0 0.3711 0.7318 0.4925 0.4037
4 0.8246 0.0 0.3881 0.6816 0.4946 0.3980
5 0.8139 0.0 0.3399 0.6522 0.4469 0.3481
6 0.8252 0.0 0.3343 0.7284 0.4583 0.3707
7 0.8127 0.0 0.3399 0.6452 0.4453 0.3453
8 0.8202 0.0 0.3428 0.6875 0.4575 0.3638
9 0.8100 0.0 0.3399 0.6316 0.4420 0.3397
Mean 0.8207 0.0 0.3544 0.6830 0.4664 0.3713
SD 0.0068 0.0 0.0181 0.0334 0.0202 0.0233
Completed trained with default hyperparameters tuned RidgeClassifier(alpha=0.105, class_weight=None, copy_X=True,
                fit_intercept=False, max_iter=None, normalize=False,
                random_state=3286, solver='auto', tol=0.001)
Parameters
alpha 0.105
class_weight None
copy_X True
fit_intercept False
max_iter None
normalize False
random_state 3286
solver auto
tol 0.001
Model Accuracy AUC Recall Prec. F1 Kappa
0 Ridge Classifier 0.8256 0 0.3642 0.7046 0.4802 0.3879
Out[27]:
LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_1 PAY_2 PAY_3 PAY_4 PAY_5 ... BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 default Label
0 50000 2 2 1 48 0 0 0 0 0 ... 7988.0 8011.0 2028.0 2453.0 2329.0 431.0 300.0 500.0 0 0
1 200000 2 1 1 40 2 2 2 2 2 ... 87003.0 89112.0 4200.0 4100.0 3000.0 3400.0 3500.0 0.0 1 1
2 50000 2 3 1 44 1 2 3 2 4 ... 16341.0 15798.0 2100.0 1000.0 2300.0 0.0 0.0 0.0 1 1
3 60000 2 2 1 31 2 2 -1 0 0 ... 31232.0 30384.0 1132.0 60994.0 1436.0 1047.0 1056.0 1053.0 1 1
4 120000 2 3 2 32 -1 0 0 0 0 ... 79589.0 81354.0 2429.0 3120.0 3300.0 10000.0 3200.0 3200.0 0 0

5 rows × 25 columns

Pickling model to lmn/my-cclassificatio-demo/Auto select best model/outputs/2020-05-29 14:35:36.111660/
Transformation Pipeline and Model Succesfully Saved